[Learning Center](/learning-center)[Data Catalog](/learning-center/topic/data-catalog) # Open-Source or Enterprise Data Catalog: How to Choose Open source versus enterprise is a decision about where operational ownership sits, how fast you need to move, and what you are willing to build yourself versus buy. Get that framing right, and the rest of the evaluation gets much shorter. **Key Takeaways** * The decision is who owns operations: your team, or a vendor's. * Open source gives you a real, production-capable foundation. * Enterprise and managed options add operational guarantees, security certifications, and AI-specific infrastructure on top of the same open core. * Six questions (governance, scale, security, lineage/quality, operability, AI readiness) do more work than a hundred-row feature comparison. * Run a two- to four-week proof of concept against your own data before you decide, and budget time before week one to stand up the platform itself if you are self-hosting. ## Article Contents * [What Open Source Gets You](#what-open-source-gets-you) * [What Enterprise or Managed Adds](#what-enterprise-or-managed-adds) * [The Six-Question Framework](#the-six-question-framework) * [The Open-Core Model, in Practice](#the-open-core-model-in-practice) * [OpenAI on OpenMetadata](#openai-on-openmetadata) * [The Short PoC Sequence](#the-short-poc-sequence) * [Frequently asked questions](#frequently-asked-questions) * [Run the Proof of Concept](#run-the-proof-of-concept) ## What Open Source Gets You [OpenMetadata's open-source project](https://open-metadata.org/) has more than 4,000 enterprise deployments, a community of 13,500+ members, 450+ contributors, and 15,000+ GitHub stars. It ships 130+ connectors out of the box. That is a production system large organizations already run. Internal benchmarking on OpenMetadata has shown it handling 2 million assets and 5.8 million tag relationships on a reference deployment (EKS, Postgres 15, OpenSearch 2.19), without falling over. If your organization is smaller than that, and most are, scale is not the reason to rule out self-hosting. Open source gives you the full catalog: search, glossary, lineage, quality checks, access control, and an API you can build against. You install it, configure it, and own the upgrade path, the database tuning, the incident response, and the roadmap prioritization when a feature you need is not yet built. Nobody pages you when it goes down. That is either fine or a serious problem, depending on your team's size and risk tolerance. ## What Enterprise or Managed Adds The commercial layer sits on top of the open core. Collate is built on OpenMetadata, contributes back to it, and [adds a governed memory layer plus managed operations on that same open foundation](https://www.getcollate.io/comparison). You get SLA-backed uptime, a team that handles upgrades and patches, SOC 2 and other compliance work already done, and [deployment options from multi-tenant SaaS to bring-your-own-cloud](https://www.getcollate.io/pricing) depending on your data residency and network requirements. Pricing runs Free, Premium, and Enterprise tiers. You can start on the free tier and add operational guarantees as your requirements grow. The other addition is AI-specific. A catalog built for humans clicking through a search bar differs from one built to ground an AI agent's answers. You see that gap in [a governed home for organizational memory and documentation](https://www.getcollate.io/context-center), where the team captures corrections and institutional knowledge once and reuses them, instead of every analyst and every agent rediscovering the same context from scratch. ## The Six-Question Framework Governance. Can you define ownership, sensitivity, and policy at the column level, and can that policy block or flag access, or only document it? Use [what "enterprise-ready" means](https://www.getcollate.io/learning-center/is-openmetadata-enterprise-ready) as the bar before you assume every catalog enforces what it displays. Scale. How many assets, tables, and tag relationships does the tool need to hold without search or lineage rendering slowing down? Ask for a cited number. The 2 million asset and 5.8 million tag relationship figure is a real benchmark you can ask OpenMetadata-based vendors to speak to. Security. Who has done the SOC 2 audit, who manages the certificate rotation, and does your security team need to review a self-hosted deployment's dependency tree every quarter? Put [evaluating governance capabilities across enterprise tools](https://www.getcollate.io/learning-center/data-governance-tools-for-enterprise) on the same review, because the two overlap. Lineage and quality. Is lineage column-level and automatically derived from query logs and pipeline code, or does someone have to draw it manually and hope it stays current? Manual lineage tends to go stale. Operability. When the ingestion pipeline breaks, who gets the alert, who has the runbook, and how long until it is fixed? Your answer decides whether self-hosting saves money or costs an engineer's whole week every month. AI readiness. Can an agent query this catalog and get an answer grounded in real ownership, real freshness, and real definitions, or does it get a stale wiki page from three reorgs ago? Most catalogs were not built with this question in mind, so an existing catalog needs added work. Use [the context an AI agent needs beyond a basic catalog](https://www.getcollate.io/learning-center/enterprise-context-layer) as the test. Include [why metadata is foundational for agentic analytics](https://www.getcollate.io/learning-center/metadata-for-agentic-analytics) when you score this category. ## The Open-Core Model, in Practice The open-core model means the catalog engine, connectors, API, and core UI are open source and free to run yourself. The commercial product wraps that engine in operations: hosting, upgrades, support, compliance, and additional AI capabilities that get built faster because a dedicated team is funded to build them. You can start self-hosted, prove the model works for your data, and move to managed later without re-architecting anything, because the underlying schema and API are the same either way. The reverse is possible too. If you outgrow a managed contract or want to bring the workload in-house, [how data catalogs evolved from technical inventories to governed platforms](https://www.getcollate.io/learning-center/data-catalog) covers what capabilities to expect either way, since the core feature set does not change based on who is hosting it. ## OpenAI on OpenMetadata OpenAI built an internal data agent, Kepler, on OpenMetadata. It now serves 3,500+ internal users, processes 580+ petabytes daily across 70,000 datasets, and cut time-to-answer from 22 minutes down to under 90 seconds. The write-up of [how OpenAI built its self-service data agent on OpenMetadata](https://open-metadata.org/case-study/openai) shows an AI-ready catalog at the extreme end of scale. That is one company's result on its own infrastructure. It is evidence the architecture can support agentic workloads at scale. Your numbers will differ. ## The Short PoC Sequence Run a proof of concept, two to four weeks, against a real slice of your own data. If you are self-hosting, treat standing up the platform itself, the database, search index, and orchestration layer that the ingestion and lineage jobs run on, as an implicit step zero before week one starts; a managed or SaaS deployment skips this entirely. * Week 1: connect your three to five most important data sources and run automated metadata ingestion. Time how long it takes and how much manual cleanup is needed. * Weeks 1 to 2: turn on lineage and check whether it is accurate against pipelines your team already understands well enough to verify by hand. * Weeks 2 to 3: have your governance lead set a real policy, such as tagging and masking a PII column. Confirm the policy is enforced somewhere in your stack, either natively or by downstream tools that consume the catalog's classification. * Weeks 3 to 4: simulate an operational failure, such as a broken connector or a failed ingestion job, and time how long it takes your team to diagnose it, versus how long a managed vendor's support SLA promises. If you cannot get through this sequence in a month, that is itself data. It tells you something about either the tool or your team's current capacity to operate it. ## Frequently asked questions ### Is open-source OpenMetadata enterprise-ready on its own? Yes. It runs in 4,000+ enterprise deployments today. Enterprise-ready here means the software is production-grade. A support contract is separate. Whether you are ready to operate it is a separate question, covered in [what "enterprise-ready" means](https://www.getcollate.io/learning-center/is-openmetadata-enterprise-ready). ### What does Collate add on top of the open-source project? A governed memory layer for organizational context that agents can query, plus managed hosting and upgrades, SLA-backed support, completed compliance work like SOC 2, and flexible deployment models from multi-tenant SaaS to bring-your-own-cloud. ### Do you lose anything moving from open source to managed later? No. The underlying data model and API are shared, so metadata, lineage, and glossary work you have already done migrates rather than needing to be rebuilt. ### How long should a PoC take? Two to four weeks against real data sources. Skip a sandboxed demo dataset. If self-hosting, add time up front to stand up the infrastructure itself before the clock starts on ingestion. Longer than that and you are probably testing your own internal approval process more than the tool. ### What should you ask about AI and agent readiness? Ask whether the catalog exposes lineage and quality at the column level in a way an agent can query programmatically, and whether corrections a human analyst makes get stored and reused. Legacy catalogs often pass feature checks and still fail this one. ## Run the Proof of Concept Run a two- to four-week proof of concept against your own data. If you want that catalog with managed operations and a governed memory layer, [see Collate pricing and deployment options](https://www.getcollate.io/pricing). 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